ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2021, Vol. 58 ›› Issue (5): 977-994.doi: 10.7544/issn1000-1239.2021.20200964

所属专题: 2021人工智能安全与隐私保护技术专题

• 综述 • 上一篇    下一篇

Windows平台恶意软件智能检测综述

汪嘉来1,2,张超1,2,戚旭衍3,荣易4   

  1. 1(清华大学网络科学与网络空间研究院 北京 100084);2(北京信息科学与技术国家研究中心 北京 100084);3(数学工程与先进计算国家重点实验室 郑州 450002);4(清华大学软件学院 北京 100084) (wangjl19@mails.tsinghua.edu.cn)
  • 出版日期: 2021-05-01
  • 基金资助: 
    国家自然科学基金面上项目(61972224)

A Survey of Intelligent Malware Detection on Windows Platform

Wang Jialai1,2, Zhang Chao1,2, Qi Xuyan3, Rong Yi4   

  1. 1(Institute for Network Sciences and Cyberspace, Tsinghua University, Beijing 100084);2(Beijing National Research Center for Information Science and Technology, Beijing 100084);3(State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou 450002);4(School of Software, Tsinghua University, Beijing 100084)
  • Online: 2021-05-01
  • Supported by: 
    This work was supported by the General Program of the National Natural Science Foundation of China (61972224).

摘要: 近年来,恶意软件给信息技术的发展带来了很多负面的影响.为了解决这一问题,如何有效检测恶意软件则一直备受关注.随着人工智能的迅速发展,机器学习与深度学习技术逐渐被引入到恶意软件的检测中,这类技术称之为恶意软件智能检测技术.相比于传统的检测方法,由于人工智能技术的应用,智能检测技术不需要人工制定检测规则.此外,具有更强的泛化能力,能够更好地检测先前未见过的恶意软件.恶意软件智能检测已经成为当前检测领域的研究热点.主要介绍了当前的恶意软件智能检测相关工作,包含了智能检测所需的主要环节.从智能检测中常用的特征、如何进行特征处理、智能检测中常用的分类器、当前恶意软件智能检测所面临的主要问题4个方面对智能检测相关工作进行了系统地阐述与分类.最后,总结了先前智能检测相关工作,阐明了未来潜在的研究方向,旨在能够助力恶意软件智能检测的发展.

关键词: 恶意软件, 恶意软件智能检测, 人工智能, 机器学习, 深度学习

Abstract: In recent years, malware has brought many negative effects to the development of information technology. In order to solve this problem, how to effectively detect malware has always been a concern. With the rapid development of artificial intelligence, machine learning and deep learning technologies are gradually introduced into the field of malware detection. This type of technology is called intelligent malware detection technology. Compared with traditional detection methods, intelligent detection technology does not need to manually formulate detection rules due to the application of artificial intelligence technology. Besides, intelligent detection technology has stronger generalization capabilities, and can better detect previously unseen malware. Intelligent malware detection has become a research hotspot in the field of detection. This paper mainly introduces current work related to intelligent malware detection, which includes the main parts required for intelligent detection processes. Specifically, we have systematically explained and classified related work for intelligent malware detection in this paper, which includes the features commonly used in intelligent detection, how to perform feature processing, the commonly used classifiers in intelligent detection, and the main problems faced by current malware intelligent detection. Finally, we summarize the full paper and clarify the potential future research directions, aiming to contribute to the development of intelligent malware detection.

Key words: malware, intelligent malware detection, artificial intelligence, machine learning, deep learning

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